However, these approaches trained with independent utterances may not be sufficient in detecting contextual natures of dialog acts within dialogs, especially with the multiple intent cases [1]}, [2]}.
First, the sequential dependency between acts are obvious in most dialog cases regardless of domains. For instance, in Table REF , we can see `Select` usually comes after `Offer`, and `Select`'s slot is usually one of `Offer`'s slots.
Second, in less stylized conversations, they usually exhibit a broader open set of less bounded purposes, subject to arbitrary changes during turn transitions [3]}. For instance in utterance 4, user presents another requests for the price even after the confirmation is done, which may result from heuristics of the `Japanese Brasserie` restaurant name, which sounds expensive.
Without such correlation matching, interpretability is undermined for joint tasks without any contextual information. Although in pipeline-based frameworks [4]}, the resolution of contextual utterances is typically addressed in the next dialog management module (DM) [5]}, dissecting dialogs unnaturally may still open the door for significant cascade of errors by neglecting contexts.
